{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cdbe4197",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = ['CUB', 'Derm7pt', 'RIVAL10']\n",
    "use_dataset = datasets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c415bffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "notebook_dir = os.getcwd()\n",
    "project_root_path = os.path.dirname(notebook_dir)\n",
    "sys.path.insert(0, project_root_path)\n",
    "\n",
    "from src.config import CUB_CONFIG, DERM7PT_CONFIG, RIVAL10_CONFIG  # noqa: E402\n",
    "from src.config import PROJECT_ROOT  # noqa: E402\n",
    "import numpy as np  # noqa: E402"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cc4dd3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_dataset == 'CUB':\n",
    "    config_dict = CUB_CONFIG\n",
    "    DATASET_PATH =  os.path.join(PROJECT_ROOT, 'output', 'CUB')\n",
    "elif use_dataset == 'Derm7pt':\n",
    "    config_dict = DERM7PT_CONFIG\n",
    "    DATASET_PATH =  os.path.join(PROJECT_ROOT, 'output', 'Derm7pt')\n",
    "else:\n",
    "    config_dict = RIVAL10_CONFIG\n",
    "    DATASET_PATH =  os.path.join(PROJECT_ROOT, 'output', 'RIVAL10')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe4b59a",
   "metadata": {},
   "source": [
    "# Load and Transform Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b037fd34",
   "metadata": {},
   "outputs": [],
   "source": [
    "C_hat_train = np.load(os.path.join(DATASET_PATH, 'C_hat_sigmoid_train.npy'))\n",
    "one_hot_Y_train = np.load(os.path.join(DATASET_PATH, 'Y_train.npy'))\n",
    "\n",
    "C_hat_test = np.load(os.path.join(DATASET_PATH, 'C_hat_sigmoid_test.npy'))\n",
    "one_hot_Y_test = np.load(os.path.join(DATASET_PATH, 'Y_test.npy'))\n",
    "\n",
    "if use_dataset == 'Derm7pt':\n",
    "    C_hat_val = np.load(os.path.join(DATASET_PATH, 'C_hat_sigmoid_val.npy'))\n",
    "    one_hot_Y_val = np.load(os.path.join(DATASET_PATH, 'Y_val.npy'))\n",
    "\n",
    "    C_hat_train = np.concatenate((C_hat_train, C_hat_val), axis=0)\n",
    "    one_hot_Y_train = np.concatenate((one_hot_Y_train, one_hot_Y_val), axis=0)\n",
    "\n",
    "class_level_concepts = np.load(os.path.join(DATASET_PATH, 'class_level_concepts.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc3226bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = np.argmax(one_hot_Y_train, axis=1)\n",
    "Y_test = np.argmax(one_hot_Y_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "85ad0c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "C_train = []\n",
    "for y in Y_train:\n",
    "    C_train.append(class_level_concepts[y])\n",
    "\n",
    "C_train = np.array(C_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9dc649-e4d0-40d5-aa23-bd8b5360bea6",
   "metadata": {},
   "source": [
    "# Prototype-Based Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d6be3f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "118ea801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14da7dda",
   "metadata": {},
   "source": [
    "## Create Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "482ea172",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_split_ratio = 0.2\n",
    "random_seed = 42\n",
    "\n",
    "if use_dataset == 'Derm7pt':\n",
    "    X_train = torch.tensor(C_hat_train, dtype=torch.float32)\n",
    "    Y_train = torch.tensor(one_hot_Y_train, dtype=torch.float32)\n",
    "else:\n",
    "    # C_hat_train, C_hat_val, Y_train_np, Y_val_np = train_test_split(C_hat_train, one_hot_Y_train, test_size=val_split_ratio, random_state=random_seed)\n",
    "    X_train = torch.tensor(C_hat_train, dtype=torch.float32)\n",
    "    Y_train = torch.tensor(one_hot_Y_train, dtype=torch.float32)\n",
    "\n",
    "X_test = torch.tensor(C_hat_test, dtype=torch.float32, device=device)\n",
    "Y_test = torch.tensor(one_hot_Y_test, dtype=torch.float32, device=device)\n",
    "\n",
    "# DATALOADERS\n",
    "batch_size = 64\n",
    "train_dataset = TensorDataset(X_train, Y_train)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "test_dataset = TensorDataset(X_test, Y_test)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497a8c4e",
   "metadata": {},
   "source": [
    "## Learn Prototypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eb8f8f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.models import PrototypeClassifier\n",
    "\n",
    "num_concepts = config_dict['N_TRIMMED_CONCEPTS']\n",
    "num_classes = config_dict['N_CLASSES']\n",
    "\n",
    "model = PrototypeClassifier(num_concepts, num_classes).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "lambda_binary = 0.01\n",
    "lambda_L1 = 0.001\n",
    "\n",
    "if use_dataset=='Derm7pt':\n",
    "    lambda_push = 1\n",
    "else:\n",
    "    lambda_push = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c7f50626",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Prototypes: 100%|██████████| 50/50 [00:25<00:00,  1.97it/s, Train Acc=99.98%, Train Loss=1.7673]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy of 99.98% achieved at epoch 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# train and test\n",
    "from tqdm import tqdm\n",
    "from src.training import train_epoch\n",
    "\n",
    "num_epochs = 50\n",
    "best_acc, best_epoch = 0, 0\n",
    "\n",
    "tqdm_loader = tqdm(range(num_epochs), desc=\"Training Prototypes\", leave=True)\n",
    "for epoch in tqdm_loader:\n",
    "    train_loss, train_accuracy = train_epoch(model, train_loader, optimizer, lambda_binary, lambda_L1, lambda_push=lambda_push, device=device)\n",
    "    if train_accuracy > best_acc:\n",
    "        best_acc = train_accuracy\n",
    "        best_epoch = epoch\n",
    "    tqdm_loader.set_postfix({\"Train Acc\": f\"{train_accuracy:.2f}%\", \"Train Loss\": f\"{train_loss:.4f}\"})\n",
    "\n",
    "print(f\"Best accuracy of {best_acc:.2f}% achieved at epoch {best_epoch}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1b82dc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7601"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_labels = Y_test.argmax(dim=1)\n",
    "predictions = model.predict(X_test)\n",
    "round((predictions == real_labels).sum().item()/len(predictions), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f21286",
   "metadata": {},
   "source": [
    "# Local Explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fd929598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Correct: True\n"
     ]
    },
    {
     "data": {
      "image/png": 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i1GqnSi6FO15A5f83/2OPPeb7WqGPR3/X629zBSKqslIBiiqRvLlUcb3X5IQ+gwYNcpVfCmp07lWBeVSg4lVTKUBUCKMWPf/cQTT+RyvSpQUvkIrdYhebimxUdONfeaYh7TqmmkOmFkCvDdA7HspnFFx5n0WtLqggdefOnWd8VrVyoX9bX4Zq3/MvdUvolFgqO1u9erUbFBe7dM3rr9Q3QXw0KV+rAHrfJEpZveRWM6q0FCcAAAAAACml9i8VTlx33XXuj3pVC2lOT//+/V34oiDKo1XhFBSopUrhlOYnq6JIQYmCBQU/3or2+ntWHT5eq5mqr1KLqpC0rQrDVKXkrfx2Nm+88YZNmDDBjcVRpY2eR6HHNddc4/aBKpU86m7SKnB6HQVStWvXdqGbwjeFeKoo03vXPlABi39gltz3qufTzCpVgKm1UvtY+1Kta6po01Bv/yHkyhVuvPFG915y5crlurEUlCnUqlu3rqUFtUV6CiexXVNztb7++mt7+eWXXbGQZnzpM6Yh58pFdAz8aWbZ1KlTrVmzZm6FPx0Xfc5++OEHu/POOy3QgqKTkhzFqkxKDKV4iaElM/WBVYmdkrzYVIKmD7DXcxubElUdCH3oNEtKH0qV36lsUgPZ9A2TlA+InktBmV43I1MrpEoY+7zVx0pVKxXozcmwtv+z3cb2Het+GOuHbGpQT6/aTRd92sfq1+TYJNfiVdutwc1j3dy4hEpf9b8A+t7W9zQhNQAAQEya96OV3ypWrBijBQsAkvOzIrG5SrLb9xIbNiWWN6DLf8lCf17ZX3xUfqd8TYlf+/btXZ+q+ioVSKlETSWS8U32Vxml16fpn1qqDzW+pSkzEpWNBlmQWbLiR4j2n1d+m1qfCX1e9ZzRFmRRHJtk0/5z+zE6OsFj41/BmRm+rwEAAFKTfj9KTscLgKwl2u9vqoT+rkrs31zJDqUSohdXP6jK7xJbDna2H3zeH/DxUai0bds2u//++10Zm0dBlMokNQRdpZVxTbV/+umn3TC02BRqKQXMyFTap2qcYrmKWejJjF31FUiRuSLdftT+3LVrV6o8pz5bes5j2YrZrqMcm+Q6lu3UsdH+TOjYeLPmdJ5axxAAACCz0Axe/R2nMSgpHa4NIPM6efKk+1mhIfb+Q+5j8x/kf05CKYVAarVT8LRv3744UzAFQIkNpdRDK94cqNh0vcrD4qPeSFU49evXL8b1GuylSikNSdfMKm/5Tn9a3cC/nUfPo77SYsWKZfj2PbUwuram4/Ute47sgd6cDGv38d1uP2p/ptYgOK0YqecMjqpvxUM4Nsm1NerUsVHpaELHRgMaAQAAEDf9B5/+iNS8oOSsgAYga8iRI4crGNLcroTa9xLbBpzsnzZqi0vsYLTE0FA4hVjecpWxW/u0ZKKCovicd955tnz5citZsuQZt2mAmfc8cdEgNJ1i045OqDorQ5XiqnfvzCIxJJL2nxe8ptZnQp93PWeQRVs2jk2yaf+5/Rj0X4slAAAAkjHyIyjIdwKAuHg/I86WlyT2b7Nk/wWnKfnaEIVBmmqvy2qV00BxtTjp6/iWnoyvUkpVTAsWLDjjNm/VvUsuuSTex3sr9C1btuyM29auXeu2R8EXAAAAAAAAAi/ZodSmTZvcudri3n77bTfzScPPv//+e3vyySfd17qcFFolbvPmzW4FPY8qIF544QVXydSlS5d4H6tlDkWzodRi5VFIpSU1r7zyyjirqAAAAAAAAJCBQikNwpPSpUtbjRo1rGDBgjZ//nx3XceOHd35jBkzkvScGlJeq1Yt69mzpw0ePNiFXS1btrSff/7ZBV1eqKSgacKECTGqolStNWjQIJs6dapbgW/MmDE2fPhwu+KKK9y8qdGjRyf3rQLIBBR2jxs3LkboDQAAAADIgKGUFxApMJILL7zQvvnmGxdWzZkzx123c+fOJD1nSEiIC7J69OhhH3zwgfXv399NdNflBx54wHe/iRMnWvfu3d25P1VUjR8/3m2DAioFUa1atXJhWVwDzgFkHdu3b3cz63QOAAAAAAi8ZA86VwXT2LFj7a677nKVSc2aNbNhw4a51a+0aoNmOJUpUybJz6sV71QhpVN8VAGlU1wUaOkEAAAAAACATFgp9eijj1rFihXdRPVKlSq5IEhL/mmFO7faW3S09erVK3W3FgAAAAAAAFm7UkpVUEuWLLGvv/7acuXKZeXLl3eDzUeMGOEqpTp16uTmQgEAAAAAAACpFkpJaGhojFa5q666yp08ERERbtU8AAAAAAAAIFVCKa91T0PNNUfK36JFi9yMqTp16viGngMAAAAAMq5NmzbZnj17LD0qWrSonXfeeYHeDADnKpQ6cuSIWwnPs3HjRjfMXOfHjh3zXa9ZUmvXrnX3X758eVK3BwAAAACQDgOpGjVr2NHwo5YeheQJsdWrVqdKMHXy5El7//337eOPP7Zly5ZZWFiYFSlSxBo1auQW+tIK75nV0qVLrV69eom+v/7+14zpDRs2uK+1IJkWQDtX26Pjctttt7nL7733nvXs2TPVXisp26HXHT9+vLu8fv16q1ChQppsR5YOpXbt2mU1a9a0EydOuK8VSEnjxo3jfUy+fPlSYxsBAAAAAAGkCikFUh2GdrBi5YtZerJ7426bNHKS28aUhlI7d+609u3b2/z582Ncv2PHDvvmm2/c6d5777VRo0ZZZrJlyxZ75JFH7MMPP7TIyMhEP27atGm+QEreeecdtyiauqoCsT2pbd68efbAAw9Y9uzZbcaMGQHbjswsR1La9e6++2575ZVXYoRSSkbj06JFi9TYRgAAAABAOqBAqlS1UpYZqQDjuuuu8wVSWk2+T58+bpbyr7/+ag8//LDt37/fXnvtNWvQoIHdeuutlll07949WaHLuHHjYny9efNm++mnn6xt27bnZHtuvPFGa968ubtcuHBhO9e8IpymTZvGuP7ll1+2J5980l0uVSpzfj+ky5lSSjwPHDjggiiVqimY6ty5s+XJk8d3H10XHBzsStv0QQKA9KBJkyZJWnwhPc9MyEiY7wAAADIKtYapMkbuu+8+e/XVV3231ahRw6pVq+YLRN59991MFUolVGwSH433+frrr93lqlWr2po1a9zlsWPHpjiUim97lD345w+BUqhQIXdCGodS2un65hMlxQqglBLHHnQOAOkxlEosBVI1a9Sw8KPpc2ZCRpInJMRWrU6d+Q4AAADnkjcjSK1nqoqK7eqrr7YPPvjA6tevb7Vq1Ypx2/Hjx11Ln+ZQeeGMFv7q3bu3m4HkdRqJ5g9pNnOnTp3cnKpnnnnGzWkeOHCge40rr7zSF5J98cUXrkWuQIECNnnyZLvwwgtt69atbm7Tjz/+6IKh0qVLW8eOHV0RScGCBWNs17Zt21xFj+67fft2NxurYcOGNmTIEN/vx/7b5n2tyqCzVU6ptU7vW/Tazz77rK1YscJ++OEH97rarthSuj2xZ0p17drVypUr58YNKRj7559/Yjz2nnvusddff91dXrlypRtJpP+ofu655+yzzz5zv/erQq5EiRKu00szscqUKeNeyzsOMnPmTLcd2u+6T0IzpRYuXGgvvfSSe4z+k1t5ieaQDR061M3f8vi/xnfffeeq8F544QX7+++/3WNU5KPXypkzp2VmyV59z79vFAAyE/3joUBqQocOVrNY+pqZkJGs2r3buk1KnfkOAAAA55KGmy9YsMBdrly5cryFF3F1Ax0+fNiFJosXL45x/dy5c91JAYwCEM0l8vfLL7/YV1995ftaAZW/+++/33UqiQKR888/3wUgl1xyiZtx5f+3uUIQtc3NmTPHBViybt06d18FNh4FQQpAFHB9++231rp1a0suzY+SkJAQu/76611YpnBJM6C82VL+zsX25MqVy4VUCsQUBur9e+GWArNPPvnEXb7iiitcICW33HJLjP0uCgnVijhr1iwXXiWXQku1ferz5D8fS/tDAeP3339vl19++RmPe+6552z27Nkx2iBHjhzpPjMjRoywzCzZoZToG2Tq1Kmur1PefPNNN3NKSZ4SxBtuuCG1thMA0pwCqfr0iAMAAGR6qlLxqn6KJfE/JTUI2wukbr75Zhs8eLCrxtHfxD///LMLQJ5//nl76KGHzvh7WtVSTz31lFvdTYHMb7/95rv94MGDrhro4osvdiGFAgpV/iiQ0sgc/f2tkEdhmiqyVq1aZY899piv7VAzob0A6IknnnB/nyu4Uduh3q8GtqsqR8+tsTwK0ERfn23kheZuLV++3F1u166d5c+f34U9qgZS651CGFWb+Q88P1fb07dvX7d/o6KiXCjkhVIKufbt2+e7j+j1FBLKHXfc4Y6VqtQefPBBmzJliqu0Wr16tXsOva6qsLzZUgqVNF8sPgoM9ToKpDTCQtmIqupUEaXX0SqOOt5r164943l+++03F0Jpnyhc7N+/v7tex59QKh5K+5TwHTlyxIVSv//+u/uQKcHVh1DfjCozbNasWepuMQAkg34x8CR2rhQAAACyBv8V3hRuJNahQ4d8bVxq15swYYKvImrSpElWpUoVFyJp7E3sUErUrqXWr+rVq59xm6p71CYmahdUcKOKItFAdrX6efdT2KF2Op0UhiiMUSAmmoOlley82VgKjBQO1a1b173XsmXLxvj9WF8nZcC5WuhEAY62RW1rqjxSyNOmTRt3m9oMz9X2aFG2li1bun2jijS9fz1erX6iFkHtH1GLnyrbFAypkl/hnqq1dJy0vaJ9p8f7v27sr+Oi96GAS9Qy6BXvqEIrPDzchZe7d++2Tz/91A3Q99e2bVtXZeZto+ZyqRXSvyIus0p2KKVSPH3QFELpA+Z9KNU3qh2ub5gXX3yRUApAuqB/GPS/E/pfCfXrAwAAAB6t5KaqHoUi/u1lsel2/+ofVdZ4QYTmA/m36OXNm9dV3Cic0iwljTRQBY3/7f6ziGJTyOVPQYoXmCl80Sk2/R3+77//ur/RvWHhF1xwQYz7dOjQwVJChSkKViRHjhwuE/DCMoVrCqVEwYoXSql171xtj9x1111uG/T+1Q542WWX+UImVWL5h1wKpVRAo5BMVWZqO4wvoEwKVbt5vIH4Hs2r8ixbtuyMx9aKNaPMq9bzbwPMrJIdSqn/VR8+lRoqefS+/uabb9ywMA1a85bSBAAAAAAgvdJsIg0RX7RokWvDUoVKyZIlY9xHAYHmTSlUUZCiKiaFMoldRS72AG9v9lN8Yt9+ttfyqBrH/7U0yDs1ff75565CzNsnmicVF81PUhVSqVKlYuyH1N4er9JIlVpqufvoo4/cuRfo+Fcl6bhqdpcyCwWEapdTcKi5XGq3TImEjk9CnwNvLpe/2PPHMrP/It4k8srIVJKmA652vnz58rlv5Hr16rnbvKFsAAAAAACkZ94QcwUIGpwdm1aiV5ihWUVaZU+0mppXhaMV6v1b/1RR5M1FUuuXijn8nW1Vtdi3KxDzaIaUttM7ab6Ttk2XNf9I9/XCD60G52/MmDF21VVXufE73t/1/kGJf4CS0IDzs1EopH3mbfu52h4vxNGMKFHFlNdSqc4t/9bIN954w+0n0XHU1z169DgjFIotMdvgDVIXFe34U2WWJ3alWFaXLaUpoOa06JtPlDjqw6PSRIn9TQcAAAAAQHqkIdVa4U40l0ghiQaYa7bPM8884xs+rfY9r6pGA741lFsUDCnYWrJkiQujNMfIC1k0xDulNIbi2muvdZcVurz11ltucLcqkjTwXDOSGjZs6AIUVQF5M6c0/1kznDQIXUO0te36G16P81YZ1Gwl/6Hb/q1o/jQEXM8nmgHlH4x5J28VQ9GYn3O5Pf4USimnUDuld39vwLnHq/ASrcyn/afKLw1K9/i3zHnboSBL26sWyvgo3PJykn79+rnn1/7ScRo+fLi7vkSJEr5ZU0hhKKVEWJQga2ibwigNF1OpowZ46eu4hrUBAAAAAJDeKIBQMOIFU6qiadCggZvtpCHUCjsUSGl1u0svvdT3uFGjRvnmP6mCSiuuqSXMm2mkcGrQoEGpso36+1vzr7RS4J133mnVqlVzQZVmp6raR7d7VUaqQPJmE2nsjuYWXXPNNW62lcIThSXefCyv20k0rNwbXp5QlVSvXr3ivI+CMe03UVvc1KlTz9n2+NN86/bt2/u+1mtprJA/tV16r6EcQ/vvpptuci2PHv/h4t526H1oe19++eV4X19D2/WcqtrS82mbVT2l46Q5VgoVtYKfgkykQiilNFiJp6b7Kz3Wh0gr7mmo2bx589x9Yk+UBwAAAABkXLs37rbt/2xPVydtU2rRXCLNRla4oOojBQn6W7dMmTIuZFAF1D333BPjMYUKFXItaaq2URijsTZ58uRxnUSqFFIQkVozghRy6LVuu+021xKoFj/NbFLwpYoirxrJW8VNf6vr73K9L91X70Mr9+m+3hByGTBggAu3ChYs6EITVV3FpllQH3zwgW8GV7du3eLdTgUxHg08PxfbE9/Ac48GnGs7/V1++eX25ZdfuuBQx0iVSyquUY6h4+a19XkUQuk46r6q9vIquRJ63/qM6LOikMx7j7fffruroNPrI6ag6MQ0R8ZBbXsKoTTYXImy0mGVy82ZM8elxjoYSkIzIqXMGip38OBB90MoI9OQN/2w6PNWHytVrVSgNyfD0j92Y/uOdUu83nLLLanynPqBrH+0Fn3ax+rX5Ngk1+JV263BzWPdUEr94xKfl156KdGr7/mOTZ8+Vr8Uxya5Fm/fbg3Gnv3YAACAwFMVkLpeKlasGKN1yqP2pRo1a9jR8KOWHoXkCbHVq1YnOrwAcG5+ViQ1V0n26nsa5qalLbXUpJay9DZGZYuzZ8+OUc4IAAAAAMi4FPYo9FGrVXqkKhYCKSDjSXYo5Yk9zFyldQRSAAAAAJC5KPQh+AEQkFBKPZAamKYWGJVg6euz0f0Tu1wkAAAAAAAAso5Eh1Lvv/++C5meeOIJF0p5X58NoRSA9EAz8CIjI1NtyCQAAAAAIIDte2ebkZ6Y0AoA0oJWvwAAAAAAZMBQStPVRctN+n8NAAAAAAAAnLNQqnz58gl+DQBAWtCS1Ol15Z+MhFWKAAAAkCHb99S2t3TpUluyZInt27fPTp48aYULF7YLLrjAGjRoYNmyZUv9LQWAFPjnn3/sxIkTljNnTqtWrVqgNwcpCKRq1qhh4UePBnpTMrw8ISG2avVqgikAAABknFBKg8uffPJJ94dBXPTL7SOPPGK9evVKje0DgFTx/fffW1hYmIWGhtrAgQMDvTlIJlVIKZCa0KGD1SxWLNCbk2Gt2r3buk2a5PYnoRQAAAAyRCh1991321tvvZXgkPONGzdanz59bN68eTZ27NjU2UoAAPwokKp/esYhAAAAgEweSn311Vf25ptvusvBwcF27bXXWv369a1QoUJulb0DBw7YX3/95aoRdFkVVbqPTgAAAAAAAECyQqm3337bnVesWNGmT58e76BzBVJt2rRxlVKqqiKUAgAAAAAAQLJDqT///NNVRD3++OMJrrxXsGBBe+yxx6xt27a2aNGixD49AAAAACAdS88r4LKqLJDJQ6n9+/e7c62udzbVq1d351qZDwAAAACQCVbArVnDwsPT5wq4efKE2KpVKVtVtmfPnjZ+/Hjf1y1atLCpU6fGuM+IESNs2LBhvq9VsLFhwwZ3+f3337fbbrvNXX7vvffc850rek11Mcmtt97qXjs9UFfV1Vdf7S6rqOXff/+1ChUqpMpzR0VF2YoVK6xu3bq+65o1a2YzZ85McO51atPiSXv37vXtf++9StOmTW3GjBlpsh1ZLpTSUura0fnz5z/rfXPnzu3OT548mbKtAwAAAACkjxVww4/ahJEdrGal9LUC7qp/d1u3oam/quxvv/1mERERvr9v5Zdffkm158+MNFvao5BIY4CeeuqpFD/vlClTbPDgwW6udaACOOUbGlGk7rHnnnsuRiiFNFp9T7Jly5aClwMAAAAAZFQKpOrXzBor4B49etR+//13u+qqq9zX4eHhNnfu3Hjvf+ONN1rz5s3d5cKFC5/TbStXrpxt3rzZXc6bN6+lB+qumjhxYozrVDGmECdHjiRHDz4bN2601q1bu8sKpfx98cUXLjhMCx999JHdc889cd7mHQv/ABOJk+RPxsCBAy1PnjwJ3kffrAAAAAAAZERqOVOL3LRp03yh1OzZs+348ePusqpk1q9fH+Mx+jv5bH8rp5bs2bNb2bJlLT1RaHPs2DF3uWrVqrZmzRrbvn27fffdd9ahQ4dkP29CbXnFiqVd1V5C25HejkVGkuSyJyWR6rNN6KT7AEB6kitXLvc/FzoHAAAAEqJZRfLzzz+f0bqnQCquNkG1lWnkjU7+LWZbtmyxO+64w82f0u+iISEhVrNmTXv44Yd9IY5n1apVdtNNN1np0qUtZ86crgrqggsusOeff97NVPIoMPNey392lS7rOg1+P3jwoN17771WqlQpCw4OtosvvtgmT558xnYvW7bM2rVrZ6GhoVagQAHr0qWLC5MUzMV+/sS07uXLly9GG9/YsWPjfYxmdrVq1coKFSrkAr1q1arZgw8+6GY2efvUv01OeYP//tVx8vaDfP31176vY7cNKlAsUqSIu61x48a+61euXGldu3Z1x1R/L2jxNs3SfvXVV337XPvAmxcmuqzn8eaJea/pfW48erxaGC+55BL3vDqeF154ob344ou+gDMlxy7LVUql1eAwAEht8ZXaAgAAALEpXFDwsXjxYteWptDEC6V0mwZ4J3Yo9qWXXuoGxfvPa169erWNHDnS/vrrL/v22299bWpNmjRxgYT/HKOlS5e6k1rERo0alajXVeCh7fzzzz991y1YsMCFTwphFP7IwoUL3XBu/26nTz/91N33yJEjlliLFi3yvdZ1111nl19+udWoUcO9TwVPem8K5fy99tprdt9998W4TtVVCuAUwKgyLamuvfZaK1OmjG3dutU+/PBDF/x5tJ+9xdj69u3rzhUqXXHFFb4QzNt3Ou466fg9+uijlhyRkZFue3766acY12s/6TRp0iQ3Kyt2+2Vij12Wq5RSaWJST4n9RgUAAAAAIL248sorfZUuWlFOYYYXEni3JYYqrbxA6uWXX3ahiyqTOnfu7K5bsmSJrVu3zl3+8ssvfYHU559/7q73QiPNdlZQo5AkMQ4dOuTCNAUiqr7SSoJeUKKwxqNQyAukBg0aZMuXL3fVRprTpMHxieVfGaWqI7nlllt8+3DcuHEx7q99otfz5mOpxe/vv/92w8xFYZ0qlTSna86cOb7Hab8pnNP18bU19u7d213W882bN893m1ddpWowVaOJ9oWOrSqk1PGlfa797C3w5gWGOnYvvfSS77l0WduhbY+PwjUvkNKKhH/88YcL77z9o3llg07vg+QcuywXSn3zzTfuG0DpZlJOAAAAAABkJGrl8trGFCz9+uuvvlau2C1aCVG7mP9qfgoZNH9I4YL+vlawUbly5TPuq9dUQFK9enX74Ycf7PDhw/bPP/+4FrvEUouYBoSrYklVWZ4dO3a48927d/sCH7WzvfDCC1a7dm1X6fT6668naSD8xx9/7C6r9axly5a+UMprq3v33XddqOL56quvXMWYPP30064KSBVAzz77rD3xxBP22Wefucerpa9kyZK+x+lr7b+EZnepVdIbrK52P+89qypJunfv7nu8qqB0HBQU3nDDDa5lsUSJEr7X9CqrVCmnk0eXtR0KweLj7UPNvVLQpyo4DWrXsa9Xr55vEPzhw4eTfOyyZCh1//33u15Wfcg6duzoygZ14AAAAAAAyGy88EnDzr3WvUqVKiVYHRObqpy8mUwKYtq3b+/CJ80sGjFiRIzuIs1y8gIdzSHSZc0h0nM899xztnPnziRtf61ateIcCO6FQf6vrcDEn9rvEsu/wuv88893+0vVRqpUUsgj27Zts++//973GFWMeZQzeBRiPfLII64Sygvrkkrte2qbE4VbaoebMGGCa4WUPn36xLi/WvgUDl122WWuikozn7zt8w/SkkLtgJol5gV+mrPlUdWbNzz/+PHjrsUxqccuS4ZSd999t0tNVUamlG/AgAHuYBFSAcgI1Muu8ludAwAAAIkNpVSx5C3mlZQqKS9kUeChmUAa4N2wYUNXXbNixQpXmVSnTh3XoidqIVM1j9oFNeRat2mus1q+hg8f7r7WbKbE0kB1T1wVPf4LAKVkfrR/a562vU2bNr6T/wqF/gPP/V/vXAQtd955p6/SSW1wXtubwre6dev67qewSqHYM8884wKigQMH2o8//ugGi6eEV6kVH//3H3S6miwpxy5LhlKjR492oZNK/CZOnOh6T1VyduDAAUIqAOme+uM1rFDnAAAAwNn4z47y5islZZ6UaBU7VQ4plFJbms7VLqbqIq/1zQtrFDipwkghmP6e1lwlFYVotpK3DR999FGqvT9VfXn85zaJ2hUTQxVFs2bNStR99d7UrihVq1b1Xe+Fcp5rrrnGOnTo4OY4xQ5tEhueaQ6TV2ml9kAvm/AGnHs0CF3VUGqT0/ypxx9/3IVpcbXUJWU7VHGllfNEz+v/fGoD9fZv7ty53WtnZYkOpTyFCxe266+/3n1A9AeeytI0b8oLpVS2p5BK7X7qlwQAAAAAIKNRm55/cJOcSikNAFdAos4jBSIKmhTMqGXMkzNnTneu0EqBiAZ1DxkyxLV1aRU5L8jxv29qUHCi1/OCEw0Z1+puajPs169fot+f/xwkhTWxT95z+Q8879Spk6+aSHOdlCFoZpYuq7JJX3tBYHBwcIwQTHO5/PdJXBQgeQGUN+xcrZCxB6RrqLg3eF2zu/TcGj6u/SBey1/s7fBW0EtohcJevXq58127drnCnblz57rB9mrn1GqKcvvtt5+x+l5Wk+RQKq4Psvo1VXqohFPlcTqY+kBfdNFFqbOVAAAAAACkMf8QSpU3Gm6dFOow8v4u1upvmrmk4eUPPPCALyjp37+/u6wqHS8EUztZzZo13Wwh/a3tDV+/7bbbLDWpisgb+u0NOtfAb/+h3nG1l3mBjTdIXGGZBogn1ErnP/Bci6JpTpY3vFuVUdovTz75pLtOrYreSnyaqeRVHamiS/vk008/Pet7075SJZKnR48eMdrivHBMtAKhcg09t/8qewrGvGDKG04uqmRTUY4XXsVFAZs3O0qD6/0Hncull17qO7ZZWcKNjomkkkKlhGrtU/KoHe8lrgAAAACAzGHVv7stK22T2vUUpCSnSkq0Wp6GpGslNg3dVoWUqmsUsui5NdS7SpUqvgHdqqZRV5K6kVQNFBER4Sq29Pe17qtxOalJQcvs2bPtf//7nwt8NGdKVT2PPfaYC47EP9jxp9lL3mpwCnT8B3L7U8CkAOb33393w7/1ON1f3VZqXVMIpLZGzXTSa6ozS4GUAjtvMPhbb73l3r+qqRSYebclRPtKAZvX8hh7wLmoNVLP9fnnn7uKJh0DtQ8qEFR1m/a/ZtLqOm2rqtn0edDgdoWECdG+VBilarIPPvjAVcnp+RS+KcDT3LBcfnO9sqqg6BRMNNM3iZJfTdFXKZ4SVKWIOjhaylGrB2RE6vFVBZhaEZOy5GZ6pG/Abt26WZ+3+lipaqfSZSTd9n+229i+Y90gPC1NmhrU/qpVNxZ92sfq1+TYJNfiVdutwc1j3QDIhFqG9Y+dvrf1Pa0Bhok6Nn36WP3T/yuDpFu8fbs1GHv2Y5Ok5+TYpNtjAwDI2I4dO+aGUlesWDFGm5JH7U01a9aw8PCjlh7lyRNiq1atPmtQgJi0EJBWA1TwpSowBUDeLKzSpUu7yw899JCrqAIS87MiqblKsiullAyq/EwfVv9cS8mv0k+VxillvOKKK5L7EgAAAACAdEBhj0Ifb85PeqOqGAKppNOgb28hIFVH3XrrrS5M8G9hYywPzqVkh1Lqd1UwVbJkSTeca+TIke56VUopjNJANlVLEUoBAAAAQMan0IfgJ3PRPCsNVpcRI0a4k78LLrjA2rdvH6CtQ1aQ7EHnGmaudr23337b7rrrLt/16of1ejbVFgAAAAAAANKfO+64w7788ku7+uqrXbVZ9uzZXUuW5idprtOMGTN8q+QB50KyP10aai4a0hWbqqdEvYMAAAAAACB90gp03ip0QIaplPKGnqliKravvvrKnSd1uUwAOFeqVq3qlnjVOQAAAAAgA1dKdejQwV555RUbNGiQK/fztG7d2qZNm+Za+7TMIwCkB/w8AgAAAIBMUik1bNgw17oXFRVlf/zxhwuh5Oeff3bXVahQwYYOHZqa2woAAAAAAICsHkoVKFDA5syZ44acFypUyKKjo91Jl++99153mwalAQAAAAAAAKnWvvfTTz9ZkyZN7PXXX3enPXv2uKn8BQsWTO5TAgAAAAAAIItIdijVrVs3O3r0qH322WduVgtVUQDSs7Fjx9rhw4ctX7581qdPn0BvDgAAAABkeckOpRRIRUREWI0aNVJ3iwDgHFAgFRYWFujNAAAAAACkdKbUDTfc4M4nTZqU3KcAAAAAAABAFpXsSql69erZ999/b0OGDLG33nrLateu7Yaf58yZ03cfrcj3zjvvpNa2AgAAAAACZNOmTW6WcHqkcTLnnXdeoDcDQFqFUoMHD3ahk2zYsMGd4kIoBQAAAAAZP5CqWaOGhR89aulRnpAQW7V6dYqCqZ49e9r48ePPer+mTZvajBkzkv06SJl3333XevXq5S7nz5/ftm3b5ubGpgaNKFq/fn2MMUUVKlSwjRs3Wvny5ePNPVLbrl27LCoqykqWLOm+1utWrFjRXb711lvt/fffN8vqoZRER0cneLsXWgEAAAAAMi5VSCmQmtChg9UsVszSk1W7d1u3SZPcNlItlfn5F74cOnTIPvnkE+vdu7elNNv49NNPbejQoS70GT58uAVCeHi4vfTSS/bss8/ad9995wulMrNkh1JKDwEAAAAAWYcCqfqlSllmN2fOHCtbtmyct+XOnTvNtwenrFq1yv74448zVtlOaSg1a9Ys69q1a5y3zZ07106ePGk5cqSopidRnn/++TgDsXLlytnmzZvd5bx581pmkuy9qtI1AAAAAAAyG1WoxBdKIX1USVWtWtXWrFljCxcutD///NMuuOCCc9IFlpbVStHxbEf27Nkz7ecx2avv+aeG3bp1s/PPP98NP1ep24IFC1Jn6wAAAAAASKeaNWvmxtY0bNjQJkyYYGXKlLHg4GD3d7HmAOk2nV544QXr0aOH5cmTxwoXLmw//PCD7zm+/PJLa968uRvWrsfWqlXLHnvsMdea5k8VNN7z7d27191HxSKq3Kpbt659+OGHZ2zf1q1b7Y477rDSpUu7+2ku0aBBg+zAgQNn3Pf48eNuO+vXr+9mNenUpEkTN8Mpdljive/YI3s0a8u73r/iZ//+/TZw4ECrUqWK2w6dKlWqZPfee6+7LTFOnDhhH3zwgbtcuXJlGzlypO82Lb4WH7XlXX755RYaGupmT2mRtqeeesq1ynn79corr/Td//HHH3fb780N00wpfa1zeeWVV3zv8aOPPorxWjt27HCLv+m2m2++OUblXfv27d3nI1euXO4zcNlll8U4Ztqnem2Ptsl/jrf3mpp9ltLjps+rZnHpM6nPnT6XV111lc2fP9/SWorqz3QwHnjggRhvdPny5e7AvPzyy+4DBgAAAABARqJwIb52rRIlSsRYdV5UsaMgSsOp5aKLLopxu0IQLwiKjIx0oYH07dvXtZ/FblF74okn7IsvvnDBiF4vtg4dOtjs2bNj/B2ugEHh09VXX+0buXPJJZe49+JRuKGZRT/99JMLSgoUKOCuP3z4sBvgvnjx4jOKUHT68ccf7bPPPnMVO0mlfdKiRQtbtGhRjOu1faNHj3btePPmzTtre9y3335ru3fvdpcV+Fx77bVu+w8ePGgff/yxC2Zit7Ypr3jxxRdjXLdy5Up75JFHbPr06TZ58uQkvx8dZ82eOnr0qAvJbrnlFt9tCibV6ucdW1HRjgKfY8eO+e6nIO733393J+nevbslR3KPm+avNW7c2NcSKL/++qs7TvqMFCpUyNJ9pZR6LpWw6gOmUKpgwYLupMu6bsCAATG+SQAgkPQDVv87oXMAAAAgIQqNNMcnrtOKFSvOuH9YWJhdeuml9tdff7kB1f5VMqJASpU9q1evts8//9xVyqiCxwuk1Hr2yy+/2LJly9zf0qL7KgCJi0IoPc8///wTI9DwX5XtnnvucYGUqq90ve6rAhJVxSj4UqWVf3jjBRvadgVICou8352/+uorN+8oOZYsWeILpB588EH7+++/3etr+7xqrthzos7Wuqf5T6q2uuGGG3z7X/vTn0I3L5CqU6eOC6EUSHn7S19rSLoquLQvPdr/Cmu84DA2BTY33XSTu6xjpoojj7d6Y7Vq1XzVV6riUiBVpEgRmzp1qq1bt85ta7Zsp+IYhW2iENI79qJt8g+N4pLc46bVBLU9ynWWLl3qut68/Thx4kRLS8kOpZRCKoBS2Zx6OFU+qJNSQJXkKZjSfQAgPVBJs0padQ4AAACkthEjRrjwo127dq4lyp9av4YMGWLVq1e36667zl2nKiFRO5eCCVXT6HdVVTLpOWTKlCkuTIrt4Ycfts6dO7u5Srq/x6uKUiWOVwWk11P1VEhIiF1xxRXWqVMnd71ax/Q3vdoEvTBF269qH/3erFBm0qRJvplKr732WrL2iwI4jzf/ScGOOq/27dvntlnblZAtW7a4fSEKUNTiKBol5IldceYfUr355psuJKpZs6aNGTPGtckpfNF+UVtfMb8VJfW15jclNND+zjvv9FW9aX95701hofTp08d333HjxrnjoaxEYZGCTa0SqdcR7QPRNnjXeV8nNEcqpcdN26W2Ro1iUuWYx7+yLl2HUkrf1IuoD5LeuKdBgwaudU+8UjQAAAAAADIKtZYpsInrFN9AbQUD8dEco9hUoSIKqhRU+POv7lf1VGxeKCMKwLzZQ5q7JGvXrvW1Eqp9y7/Sy5tjpKDk33//daGX11qm4Ma/1UvtcF7FkCqC1PaV1EHdmmP16KOPum1UdZKqjBSY6H0rDInr/cX23nvv+d6P3rsCN53UQqc5UaJ5SP7PpZZKj/8x0/1VJaYWSM14So5GjRrZhRde6C57c668KjWFWbEr3FT19uqrr7q2TgVPaqv0b+dMjpQeN//PkH8o532G0n0odeTIEXeuSqnYNLBMvMFhAAAAAABkZt58psTeltAMJf9wJ/YwcVHVkz+vFSwxz+1PM5rOdt+zbYt/qOI/Nyl2FZnaHocNG+baHNVSqDY2VS0p3FHLY0Kvr6HdHrXctWnTxp2uueYaN1cproHn/tt9LoIWr1pK70tVUl5lVseOHWNUyqltUwGRQikNIVdAp7a/5AZinpQeN//PUHJmhQU8lPKSXJWFxfb111+7c5WkAUB6oP8d2LVr11n/dwcAAABIjtjDz892m1rJRDOW1J7mb9q0ab7L8VVmJcS/eKR3794xKr3UYrZp0yZ3WcOuVVTitapp2LVXkeQVo2hgtqiVTHOIxL+1TXOIPHre2DTmZ+bMmfbbb7+5OVI612P0WgpG9HoJtQYqwNHw7cTQzCyvOEatjR6FRv6r1el933jjjb4qJ//QJq5qr7horpVCJtH70vv0H3AuquRSICctW7Z0lWIakq52xdirKyZ1O5Jz3NKjZIdSbdu2dTtJSadW2VMQpZMu6zrtTN0HANID/YOj/4nx/uEBAAAA4qO5OgqK4julhl69evlCEs19UrCgwGjw4MH2/fffu9tUDRRXd9LZqEVMq9OJ5g6pgkjtbHpetY6pgKRhw4bub3oFK5pPJXp9DQLXcHKFGpo/5c0Y0t/6Hv8qH1UAKRDRrKhnnnnmjG3RSn/NmjVzc5Z69uzpAiKFVzp5K9UlFOhp9pFHg7vjaqn0sgetxKd2RfGGkXuhkYI+tdHdfffdbrU/DRaPiIhwt6tyy6PKJ528lf7iozZAb2i6nk9q1KjhVsPz6Nh6r6F2TW+w+G233eYL806e3gext0NDy73njUtyjlt6lLiavjj873//c4O0lAbqDz2dPPpQaJiZJusDAAAAADKHVWf5Qz2zbFN8K68ltZomIbfffrvNmDHD/V2tFdQ06NyfAg7NUkquZ5991s151iBtr9XMowol3e5V5owaNcqFSgo3Pv74Y3fyp5Bj0KBBvq+7dOni2zYNDVc1kFd5FbuqSavCqbXthx9+8J38qdrnoYceivM9aNu9TizNPfKCttj0/rzn1cBzhT5qE+zfv78LzVSNFnsVbu1vb/aT9rW2QwGSgi+dtPqdF/rER6/rn4X4Dzj32jabN2/uArGdO3fGCKziGixe7/QqeKJwUtTtEZ+kHrdMFUppMJnK6PQBU9oYe2UBHcASJUqkxjYCAAAAAAJIM3LyhIRYtzjGt6QH2rbYK96ldwqENHS8ffv29vbbb7vKGM1H0mBwtZYplPDaw5JD7YGqSnriiSfs559/dqGI9pEqpdRC5r9gmVbD033VRqcASSGOqp+0GqDa/xSg+beWKeBRmKbKKFVglSpVynr06OGqcvyHZnuzj7TSnSqeVLWlWVKqaNL9FBxpVUL/bfGn1/AqjVQJFF9FlWZLqfpL1VeqFPrrr7/ctmthNg0XV3DkDUHX/lXr3X333edWPvTCI62G+MILL7hQTXmHf9VSfPQaeg8K/+IacO7NwFLo9uOPP7r3re1U2KX9OWLECLc/Vq1a5Y6X9qv2ofaXCoDUgqgWwPgk9bilR0HRKYx49XD1RaoETR827Ugtqxh70FpGojI6fSj1gfFfkjEjUk+tlsns81YfK1WtVKA3J8Pa/s92G9t3rPuheMstt6TKc+p/Q7Ra5aJP+1j9mhyb5Fq8ars1uHms+0c8vn/MREvl6ntb39MDBw5M3LHp08fql+LYJNfi7dutwdizH5skPSfHJt0eGwBAxqYB1VpxTn+wx/fHuP7gT6/zORW2MNMYSB8/K5KSqyS7Usqj1E3LDyqI8krLMnIgBQAAAAA4k0Ifgh8AqSlF6dGUKVOsevXqNn/+fN916idV+97UqVNTY/sAAAAAAACQCSW7Ukp9mup91aR49WtqoJlovpTKOjWATH2VmugPIH6r1qfPEuiMgv0HAAAAAFkslHr66aftxIkTbpW94sWLx5jurpBKvca6j6bWAzjT9u3bLShbkHUbMjHQm5LhaT9qfwIAAAAAskAopQnvmiel6flt27b1XX/33Xe7gVe6bsGCBUl+Xk2YHz58uH377bduPlW1atXcMo6aGp9UmuKv1QC00oCWYQTSkwMHDlh0VLR1GNrBipWPuUIFEm/3xt02aeQktz8BAAAAAFkglFJ4JFqiMDaFUqJQKSmOHDnilkBcvny59evXz2rUqGGff/659erVy3bs2OGWrUysmTNn2nPPPZek1wcCQYEUKyMCAAAAALKaZIdSpUqVcrOj1J6niiR/H330kTsvWbJkkp5z9OjRtmTJEvf4rl27uut69+5trVu3dgPUu3fvbuXKlTvr86hiokePHpYzZ06LiIhI0jYAyJz0syQ6OtpVeAIAACBu+n0JANLqZ0SyV99r06aN25hHHnnEWrVqZf/73/9s4MCBdsUVV9jIkSPdH366T1KoFVBhV5cuXf7bwGzZbPDgwXb8+HH7+OOPE/U8d911l0VFRdmdd96Z5PcFIHPKnz+/hYaGunMAAADElD17dneuucEAEB/vZ4T3MyNglVKPPvqoq5LavXu3TZs2zZ08Cqs0/Fz3SayDBw+6lfu0ol/sSoZGjRq583nz5p31eT788EP77LPP3Byp2bNnJ+k9AQAAAEBWpC6T3Llzu7/L9J94VJcDiE1Zj35G6GeFfmYEvH1vzpw5ripJgZRXwqUfXhoqPmbMGCtdunSin2/r1q3uOeJqz/OqG9avX5/gc+j2e+65x+6//367+uqrEx1KqcXPv80vLCzMnavaSqeMTtVmQRZkRiVusmn/aT9Kan4mODbp89joZ5GeMzooyDL+T4DA0f5z+zE6mmOTBY4NACDj08rqWtF48+bNVqBAAfdHJ+EUgOjoaFchpUBKs8CVB53td8jE/o6Z7FBKKlWqZFOmTLF9+/bZunXr3HWVK1d2P8ySSm9O8uXLF+ftefLkcW8+PpGRkdatWzcXaj399NNJem3dXzOrYlMV2LFjxywjU0ldgwYNrFiuYhZ6MjTQm5NhReaKdPtR+zOpA/zjw7FJ22OzdOlS94NUv1zVq1cvwefU972e81ixYrYrlGOTXMciTx0b7c/U+r7h2KTfYwMAyBxCQkLs8OHD7u8zAikA/sGU/uZSNqPfIc+WlRw6dMjOSSj1xx9/uMoozXnSDyz55Zdf3Ep3q1atcoFU586dbfjw4Uma3XK2YVne/47H56mnnrKFCxe6Fj+VkiWFBrVrHpZ/pZTCrWLFirkqrYxMYd2iRYus/vH6lj1H6vR8ZkW7j+92+1H7U62pqYFjk7bHRqt66ntb39Na5fNslZt6zuD69a14KvVKZ0Vbd586NsHBwan2fcOxSb/HBgCQueg/8/T7FQCIAqmktOzp98xUD6X69etnb775prusNjmFUuPGjbO+ffv6gqPw8HB75ZVXbMaMGa69L1euXIl6bi/A0uPjousrVqwY520Kop544gkbNGiQlS1b1vbs2RPjufSHqK5TYBZXsKUQK64gS/dNKAjLKFQ2F63+MP6jI9m0/7zyw9T8THBs0u7Y6H/6vNPZjqHuo+cMUhie6lucdWj/uf2YiH2e6Ofk2KTbYwMAyFyS+h/9AOAvsb9jJvo30S+++MLeeOONGBVNaqdTxZR3Xfny5e28885zX//555/2+uuvJ/bprUKFCu6X4y1btpxxm0pHVUIa17wp+emnn+zkyZP27LPPuuom7/T888+72zt16uS+3rRpU6K3BwAAAAAAAOdOokOpd955x50rdNLqdgULFrRvvvnG12tct25dW7Nmja1du9atoKdgauLEiYneEFVK1axZ0xYsWHDGbd6qe5dcckmcj+3Ro4dbbS/2qXv37u52tRbq65IlSyZ6ewAAAAAAAHDuJLp9b8mSJS58GjVqlAudZPLkyb7be/XqZTlynHo6VU99++23tnr16iRtjAaVDx061D755BPr0qWLu07tBS+88IIrH/Wui2vguk6x/fbbb+78wgsvdCsCAgAAAAAAIIOFUvv373fnderU8V3366+/+i5fffXVvsteRZK3ol5i3X///TZhwgTr2bOnLV682KpVq+aqsjRIXa143vMuW7bMnc4//3x3AgAAAAAAQCZt3/MGke/bt8+da2aUVkFS9VSJEiWsVq1avvuqjU/y5cuXpI3R4HQNSFc73gcffGD9+/e3vXv3ussPPPCA735qC1RrXlLaAwEAAAAAAJABQ6nq1au78zFjxtihQ4ds+PDhvtvatWsXo6Jq2LBhLqyqXbt2kjdIA8nffvtt27lzp1s9T22D3mwoj15bM6v8tyEu3v1o3QMAAAAAAMigoVTnzp1dwDN+/Hg35Py7777z3XbnnXf6QiC13C1cuND3GAAAAAAAACDZM6Xuuece+/LLL23OnDkxrn/ooYesfv367rKqmtRuJxdddJHdddddiX16ADinihQp4hZMSGpbMYDE27Rpk+3ZsyfQm5HhFS1a1K12DAAAkNklOpTKmTOnGzj+xhtvuLlP+uOua9eudv311/vuo0HkefLkcYPKR44c6R4DAOnBrbfeGuhNADJ9IFWzRg0LP3o00JuS4eUJCbFVq1enWjBFWJh6CAwBAAhQKCXBwcE2YMAAd4rLE088Ya+99prlypUrtbYPAABkAAo9FEhN6NDBahYrFujNybBW7d5t3SZNcvszNcIPwsL0HRgCAJDVJSmUOpvixYun5tMBAIAMRoFU/VKlAr0ZOI2wMP0GhgAAIJVDKQAAAKQ/hIUAACA9IpQCkCV89dVXFh4e7ubederUKdCbAwAAAABZHqEUgCxh48aNFhYWZqGhoYHeFAAAAACAmWUL9AYAAAAAAAAg6yGUAgAAAAAAQJqjfQ+p4tjhYzb93em2evZqCz8YboVKFbIG1zawRp0aWVBQ0Fkff2DHAZs5fqatnb/Wjh05ZoVLF3aPv+i6iywo23+PP370uM3+aLb9/dvftn/HfsuZO6eVr1ferrr9KitWgVWFAAAAAADIKAilkGJRkVE2YfAE27p6q++6PZv22JTXp9ihvYesRd8WCT5++5rt9sGgD+zYoWO+63at32U/jfrJwvaEWfPezd11kScj7b3+79mONTt89zsZcdIFYesXr7c7xtxhRc8rek7eIwAAAAAASF207yHFlk5Z6gukmt7a1O5+/26rfFFl9/Wcz+fYvq374n1sdFS0TXxqogukgvMHW+fhne3Od+60884/z93+x6d/uMorWf3bal8gVeeqOu51rn/oeldJFXEkwmZ9OCsN3i0AAAAAAEgNhFJIseW/LnfneQvltaY9mlqx8sWseZ/mvtBp5cyV8T52/ZL1tmfjHnf5im5XWK2mtaxEpRKuuuqCNhfYZbdcZhHhEe52/3Cr9b2t3evUa1XPqlxUxV23/Z/t5/R9AgAAAACA1EP7HlJs29/b3HnxisV9858ULGXLkc2iTkb5bo/LhqUbfJfL1i5r0dHRdjTsqJWqVsque/C6GPdVCOXxn1MVbdHuXJVWAAAAAAAgYyCUQoqobc6bBZWnQB7f9QqnQvKH2JH9R9wQ8/js3bQ3xhypiU9OdPfPGZzTVUq1vLOl5ch16mNarUk1q9Koiq2dt9bNq7q82+WubXDdgnXu9not653DdwoAAJC6Nm3aZHv2nKoYR8oULVrUzjvv1PgHAEDGQSiFFNFqeJ4cOWN+nLLnzH7GfeIKtTw/vPyDa/eTE8dO2IJJC+zA9gPW9emu7rps2bNZu4HtbPyA8bbs52Xu5ASZNe3Z1Bq2b5i6bw6ZSv369S0iIsJy584d6E0BAMAFUjVr1LDwo0cDvSmZQp6QEFu1ejXBFABkMIRSSBG126Xo8adb76RQqUJ20xM3Wd6Cee2b576xNXPXuNO6heuscsPKroJq3N3jXPVVrCexv3/7285vfr4VLlM4RduDzKtZs2aB3gQAAHxUIaVAakKHDlaz2H8jCpB0q3bvtm6TJrl9SigFABkLoRRSJFdILt/lkydOxrjt5PGTZ9wnocc3ubGJm0slLe9q6QIp+Xfhvy6Umj1hti+QanNfG7ug9QW2Z9Me+3zY57Zj7Q779JFP7a537vLNtQIAAEjvFEjVL1Uq0JsBAEBAsPoeUiQ4X7DlznuqHUoDyj1qw/NmTRUsWTDex6s6yqPV+zz+jzl2+NTzbFm5xZ0ruLq4w8Uu0CpdvbQ16tjIXb97w27bvXF3Kr47AAAAAABwrhBKIcVKVinpznes22FRkVG+oeXeZQVH8SlXp5zv8vY1232Xw3aH+S6HFgv1zZSS48eOx2gb1Nexq7MAAAAAAED6RvseUqxW01q2celGCz8QbjM/mGl1r65r09+Z7m5TK51ulyMHjljkicgYQVPVRlUtf9H8dmjPIZv7xVwrWq6oC7mmjZ126smDzGpcXsNdrHxRZdemp+HnU8ZMsYbXNrS9m/favK/muduD8wf72v+A2F566SULCwuz0NBQGzhwYKA3BwAAAACyPEIppFiDdg1s6dSltm31Npv1wSx38p8T5Q0f/2L4Fy68kmG/DnPnOXLlsOsevM4+efgTt+LepJGTYjz3pV0utRKVSrjLl3W9zP6Z849r05v35Tx38gkya92vtXs+AAAAAACQ/vEXPFIse87s1uOFHjbj/Rm2YsYKCz8Y7mZCNbi2gTW+ofFZH68KqNtfu909ftNfm1w1VbEKxaxRp0ZWr2W9GPOr7hhzh6uMWv7rctu/bb9r6StTo4wLryo1qHSO3ykAAAAAAEgthFJIFRp23qpfK3eKT89XesZ7m+ZOdX2661lfR8PNL+92uTsBAAAAAICMi0HnAAAAAAAASHOEUgAAAAAAAEhzhFIAAAAAAABIc4RSAAAAAAAASHMMOgcAAAAAP5s2bbI9e/YEejMyhaJFi9p5550X6M0AkE4RSgEAAACAXyBVs0YNCz96NNCbkinkCQmxVatXE0wBiBOhFIAsoWPHjnby5EnLkYMfewAAIH6qkFIgNaFDB6tZrFigNydDW7V7t3WbNMntU0IpAHHhrzMAWUKFChUCvQkAACADUSBVv1SpQG8GAGRqDDoHAAAAAABAmiOUAgAAAAAAQJqjfQ9AlrBhwwbfTCla+QAAAAAg8AilAGQJEydOtLCwMAsNDbWBAwcGenMAAAAAIMsjlAIAAAAAZAibNm1yq/kh5YoWLZqqqyJybNLvsUnPCKUAIEAOHjtmj0yfbhNXr7Y94eFWqVAh69uggfVv1MiCgoISfOzfe/ZYjddfj/f26GHDYny9bOdOGzFzps3cuNGOnjhhNYoWtQcuucRurlMn1d4PAADAuaTQo2aNGhZ+9GigNyVTyBMSYqtWr06V8INjk36PTXpHKAUAARAZFWUtJ0yw+Vu3+q5bvWePDZgyxbYfOmTPtmiR4OMVMiXW5LVr7fpPP7WIyEjfdYu2b7cuX33lgrG+DRsm810AAACkHVXhKPSY0KGD1SxWLNCbk6Gt2r3buk2a5PZpagQfHJv0e2zSO0IpAAiA8UuX+gKpYU2b2k21a7tAasq6dfbinDnWp0EDq1y48FlDqaJ58tiSvn3jvV9YRIT1mDTJBVLnFShg46691gqFhLjrVu3ZYw9Pn2531K9v2bOxGCsAAMgYFHrUL1Uq0JuBOHBskFSEUgAQAJ8uX+7Oi+fNa481bWrZgoLsmebNXSgVGR1tX6xcaQ9ddlm8j1+2a5c7V8tf2dDQeO/3xYoVtjs83F1+uVUra1G5srv8XIsW9u3ff1vp/PntYESEFQ4JSeV3CAAAAAAJI5QCgABYuG2bO69bvLgLpOT8EiUsZ7ZsdiIqynf72SqlFEqdjIqy/UePWpE8eXzP5ZmxcaPvcpOyZS0qOtr2hodbmypVrF21aufgnQEAAABA4hBKAUAaU0vd/mPHfO13HgVKaq3bdeSIbThwIMHHbzx9+6yNGy306aft6MmTVig42O5q2NCGN2tmObNn9w1E90xfv94GTp3qnr9gcLDd36iRPXq6SgsAAAAA0hpDRAAgjR0+ftx3OXeOmP83kPt0mHTI7z6x/bVzp0Wfvrzt0CEXSImCrpG//eYGmHvUmufp8fXXLpCSA8eO2fCZM+2+n35KpXcFAAAAAElDKAUAaSw62ouUkkdDyxuXLWvlCxSwN9u2tb0PPmir+vXzDZX8atUqm/bvv2e81kWlS9uG/v3dSZdlzIIFMaqpAAAAACCt0L4HIEsYOHCgpRf5cuXyXY44XeXkOXb66/x+94ntqooVbU6vXjGu06DyZ66+2lpOmOC+nrJ2rTWvVCnGaz3erJmVL1jQXX7iyiut9UcfuYqrX9avt+pFi6bSu8scDh47Zo9Mn24TV6+2PeHhbnZX3wYNrH+jRhZ0lnZHhXw1Xn893tujhw2LUTU3cvZs+3zFCtscFuYG399Qs6Y9fuWVFpo7d6q+JwAAACC9IZQCgDRWIDjYBQ6aDbX36FHf9RpC7s2aqnA6PEoK/1X4vJY+hSlLduxwlxV4ePyfX618+E9kVJQL9+Zv3eq7bvWePTZgyhTbfuiQPduiRaKG0J/NichIa/HhhzZ3yxbfdVvCwuyVefPs982b7Y9evSxHNgqaAQAAkHnx2y4ABMCFJUu686U7drgQRJbv2uVW0pOGp9vr4vLc779bk3fescqjRrmKHs+K3bt9l6sXKeLOLy1Xznfd4u3bfZdVlRNXmAWz8UuX+gKpYU2b2sq777ZWlSu7r1+cM8fW7duXqFBKQ+w3DxhwxsnzyfLlvkBqYOPG7nVUjSULtm2zNxYsOGfvEQAAAEgPCKUAIAA616rlzneHh9uImTNdJc7D06e767IHBf13+5EjrnpGJ4/CDoUZ/+7fb10nTnTBlmZIPfjzz+52VWF1qVvXXb65Th3LmzOnu6zn//6ff2zJ9u2+1wrJkcNaV6mSxu8+fft0+XJfZdljTZtazWLF7Jnmzd11kdHR9sXKlQk+ftmuXb4qNQV+sU+ehdu2uZUPzytQwJ5v2TLG68iMjRvP0TsEAAAA0gfa9wBkCTNmzLCIiAjLnTu3NWvWLNCbY70bNHAVOaqIGTFrljt5BjVpYpULF3aXO3/xhc08HU54s4hurVfPBSOT1661H9escSePph2NbtPGBVdSKn9+e61NG+v17be288gRu/aTT2Jsx0utWsVo68OpsEjqFi/uQiM5v0QJy5ktm52IivLdfrZKKYVSqnzbf/SoFcmTx/dcnlFt2rgQamtYmO+2Q36rJer1AAAAgMyMUApAlrB48WILCwuz0NDQdBFK5cqe3ab16GHDZ8ywz1ascMO0NefpzgYN7P7GjRN8bPZs2ezrm26yNxYudMHW2n37XHVVo7Jlbehll1nTChVi3P+2Cy+0cgUK2FOzZ9uCrVvdcPMGpUrZQ5ddZtdUrXqO32nGojlf3lwvL9gThUaFQkJs15EjtuHAgQQfv/H07bM2brTQp592870KBQfbXQ0b2vBmzSxn9uy+++fJmdOqnm611OPu+ekn320KHwEAAIDMjFAKAAJEbXaqVNIpPjN69ozz+tw5crjw6mwBlkcr8emEhGk1PP997C/36TDpkN99Yvtr504X+sm2Q4d81yvoGvnbb/b33r325Y03nvE4tVV6VWwabv58ixbWhsAQAAAAmRy9AQAAnBYd7UVKyRMRGWmNy5a18gUK2Jtt29reBx+0Vf36Wf1SpdztX61a5eZ/xeZffaVGvl83bLD1+/enaFsAAACA9I5KKQAATsuXK5fvcsTJkzFuO3b66/x+94ntqooVbU6vXjGuKxwSYs9cfbW1nDDBfT1l7dozqtaurVbNWlSq5Abe3/Hdd/bt33+7AfYr+/VzLX4AAABAZkSlFAAApxUIDnZtlbL36FHf9VHR0b5ZU5r9lVT+q+5pxlRs5QsWtOpFi9p1NWrYA02auOs2HjxoMzdsSNb7AAAAADICKqUAAPBzYcmSbsVDVSpFRkW5wfLLd+1yK+lJw9Kl433sc7//bpNWr3YD0Rf36eNCLlmxe7fvPtVPDzYf+ssvtnL3biudP7+NadvWd/vxyMgzqrMAAACAzIhQCgAAP51r1XKh1O7wcBsxc6Z1qVvXHp4+3d2mVQ51u+w+csTNkPKvhNKKfXO3bHGXu06caCOvuso9z4M//+yuUxWWnk+0auI3f//tZkhVLFjQVUlpUPrLc+f62gSbxVpJEQAAAMhMCKUAAPDTu0EDG790qS3Yts1GzJrlTp5BTZpY5cKF3eXOX3zhwiuJHjbMnd9ar559sXKlTV671n5cs8adPAqfRrdp44IrebZ5c5u9aZPtOHzYHpw2zZ08WoFvXPv2VigkJM3eNwAAAJDWmCkFAICfXNmz27QePWxA48autU5fVytSxF5q2dKead48wceq1e/rm26yl1u1sgtKlnSD0wvkzm0tK1e2X2+91brXq+e7b8VChWzZnXfawMaNXUtfcI4cViQkxDrUqOGGpd9Yu3YavFsAAAAgcKiUApAllC9f3sLDwy3P6SoVICFqs3upVSt3is+Mnj3jvD53jhx2f+PG7nQ2xfLmtRdbtXInAAAAIKshlAKQJXTq1CnQmwAAAAAA8EP7HgAAAAAAANIcoRQAAAAAAADSHKEUAAAAAAAA0hwzpQBkCePHj7fDhw9bvnz57NZbbw305gAAAABAlkcoBSBL2Lt3r4WFhVlERESgNwUAAAAAQPseAAAAAAAAAoFQCgAAAAAAAGmOUAoAAAAAAABpjlAKAAAAAAAAaY5QCgAAAAAAAGmOUAoAAAAAAABpLkfavySAtHTs8DGb/u50Wz17tYUfDLdCpQpZg2sbWKNOjSwoKChJzzVj/Ayb+f5Md7n/J/2tYMmCvtsiwiNs3sR5tuLXFbZ/237Llj2blalZxi6/5XKrcEGFVH9fAAAAAICMjVAKyMSiIqNswuAJtnX1Vt91ezbtsSmvT7FDew9Zi74tEv1c29dst9kTZsd5mwKpcXePsz0b98S4/t+F/9r6xeut0yOdrPaVtVPwTgAAAAAAmQ2hFJCJLZ2y1BdINb21qQuGFEitW7DO5nw+xxq0a2CFyxQ+6/NEnoi0r5/52qJORsV5+7yv5vkCqYs7XGwXXX+R7d6w275+9ms7Hn7cfnz1R6txeQ3LniO7BUrTpk3t+PHjlitXroBtAwAAAADgP8yUAjKx5b8ud+d5C+W1pj2aWrHyxax5n+buuuioaFs5c2WinmfG+zNs17+74r19zdw17jw4f7C16tfKip5X1GpeUdPOb3G+u15tgwk9Pi00aNDAmjRp4s4BAAAAAIFHpRSQiW37e5s7L16xuAVlOzU/qkSlEpYtRzZX9eTdnpCtq7ba75/+7i6fV/c82/TXpjPuc/1D19uBnQfsZMRJN0vKEx0dHedlAAAAAADSXaXU3r177d5777Xy5ctbSEiI1atXz959990kPbZChQquRadYsWLWuXNnW7Vq1TnfbiC9iTgSYccOHXOX8xTI47te4VRI/hB3+cCOAwk+x8njJ13bnqqq6rerbxXrV4zzfkXKFbHKDStb9Uur+65TdZSGnkuOXDlclRYAAAAAAOmyUurIkSPWokULW758ufXr189q1Khhn3/+ufXq1ct27NhhQ4cOjfexx44ds2bNmrkA6rbbbrOGDRva+vXrbcyYMTZ58mT7/fff7fzzT7USAVnB8aPHfZdz5Iz5rZ49Z/Yz7hOXX8b94gajFyhRwFre1dLNoUrsa3/68Ke+UOyCNhdYzuCcFkiHDh1y1VpacTB//vwB3RYAAAAAQDoLpUaPHm1Lliyxjz76yLp27equ6927t7Vu3doef/xx6969u5UrVy7Ox7700ksuzBo3bpwLsTw33nijNWrUyAYPHmxTpkxJs/cCBFpK2+XUpqcB5tJ+cHvLnSd3oiu0PnroI9u8YrP7WoHW1XdcbYH29ttvW1hYmIWGhtrAgQMDvTkAAAAAkOWlq/a98ePHW6lSpaxLly6+67Jly+YCJa2a9fHHH8f7WAVOatnr2bNnjOvr169vtWvXtlmzZp3TbQfSm1wh/60yd/LEyTPa8mLfJ3alk3/bXqUGlRL1mkcPHbUPHvjANi8/FUiFhIZY16e7WnC+4BS8EwAAAABAZpRuKqUOHjxoq1evtvbt27v2Gn+qdJJ5805VbcTl008/tV27dln27NnPqBaJ63ogs1MQlDtvble5dDTsqO96BU1eW13BkgXjfKwGoO/ftt9dXvz9YneK7dUur1r5euWt5yunguCI8Aib8OAE27Z6m2/Fv+4vdHdD1gEgtRw8dswemT7dJq5ebXvCw61SoULWt0ED69+o0Rm/P8R2IjLSXp4718YvXWrr9u2zAsHB1rZqVXvqqqusVKy23sPHj9vTs2fb13//bev377c8OXPaFeXL25NXXWW1ijEjLxDHpdn779vMjRsTfK73rrvOel5wQaq9LwAAkEVCqa1bt7oAKa72PLXbaAaMZkTFRxVWOsX24Ycf2vbt261t27apvs1AeleySknbuHSj7Vi3w6Iio9zKeLvW73KXpXT10qnyOvrenfjkRF8glb9ofuvxYg8rel7RVHl+AJDIqChrOWGCzd+61Xfd6j17bMCUKbb90CF7tkWLBB/f5auv7Cu/xU92HTli7/35p/26YYP92bevC0O8kOSK996zJTt2+O579ORJm7R6tf2yfr3Nv+MOq16Un29pfVwSIzR34lrNAQBA+pCuKqUkX758cd6eJ08eNwg9KZYuXepW48uRI4cNHz483vtFRES4k0dzZyQqKsqdMjq1QAZZkFnKRgxladp/2o+Smp+Jc31sajWt5UKp8APhNvODmVb3qro2fdx03yp8ta6o5V77yIEjFnki0l0fWizUytYsawM+G3DG8835Yo7N/XKuu9xrdC8rVLqQe/zSKUvtnzn/+J633cB2lis4l4XtOvW95K0AqFX4AnVsFJx5p7MdQ91HzxkdFGQZ/ydA4Gj/uf2YiH2e6Ofk2GTpY/Pe0qW+4OOxpk3txtq1beCUKTZ13Tp7cc4cu6NBA6tcuHCcj521caMv+OhSt64Nvfxy+3LFCnt85kzbcOCAO3+hVSt3u6p9vEDq5jp17JErrrAF27bZHd98Y2ERETZi1iz7sGPHDHFsMtNx+axzZ4uIPPVvlefAsWN2xbvv2sGICLupTh27vmbNc/Y+M+KxySo4NukXxyb94thkrd/TAiGx254jowxl9j7kibVw4UJr06aNC5hef/11txpffJ5++mk3SD223bt3u1X9MjK1LTZo0MCK5SpmoSdDA705GVZkrki3H7U/1Q6aUY5Ns1bNbPmU5bb5780264NZ7uS77YZmVqFEBbOTZhOGT7B1y9a561+c+qKbNle40Jl/QIQG/7edJUNLWuF8hd3j5302L0Z74CdDPznjsXc9f5dVqVclYMdGq+8p2FYbydmOob7v9ZzHihWzXaF83yTXschTx0b7M7W+bzg2WfvYfLh6tTsvmi+f9W3X7tTcyeuus6kvvWSR0dH2/rp1dm+FCnE+dvqePZYze3bLFhRkT3TpYnlz57a+lSrZK/Pn28GjR23ali2+7V4aHu573MOdO1uRfPmsdeXK1uzvv+2XVats/s6d5+w9pvaxyUzHxUJDLfYkxGc//9wFUqULFHCP35WEqqqscGyyCo5N+sWxSb84Nlnr97RA0N9fGSqU8pZoD/f7RdCfrq9YsWKinuu7775zw9L1mFdeecXuvvvuBO8/ZMiQGKtxKchSG2GxYsVc62BGFhkZaYsWLbL6x+tb9hzM1Uqu3cd3u/2o/Vm8ePGMc2xymN3ywi02Y/wMW/HrCgsPC7eCJQpag2sbWOMbGltY0KlKppNB/w1CD8vxX3VTbBHZ/qsoPJzjsKtCPLzvsO3Y8F+LS3zCs4cn+Nzn+tjoZ4zCbZ2f7RiqnVjPGVy/vhVnHl2ybd196tgEBwen2vcNxyZrH5u/Nm1y5/WKFbOShw+7y83y5bOc2bLZiagoW71+vRU/Xe0c2/AGDWxIvXr27/79VlHV0RERdjwy0iJOnHC354mO9j22od8co+KHD1vR0//Tl/P0fYvlyhXv66S3Y5OZjktcVVafnJ43+lKLFlb5+HEznc6RjHhssgqOTfrFsUm/ODZZ6/e0QND2Z6hQqkKFCq6CYcuWLXG29h0+fDjOeVOxvfHGG65lT1UTmid1yy23nPUxuXPndqfY9D99SanOSs9lc9HqsUp4zigSoP3nlR+m5mciLY5N7ny5rVW/Vu4UH29Y+dk0u62ZO/nLVySfDft1mKX3Y6OfL97pbMdQ99FzBqlCM9W3OOvQ/nP7MRH7PNHPybHJssdGbXP7T1cvF82Tx/caqrApFBLi5hBtPHAgwdcOyZHDap8eUr7j8GF7dPp0O3byVCiv4djeY6+rVs3aVKliP61da4OmTLGHL7/cFmzdaj+vO1VRemu9eufs85faxyYzHZfYRsyY4brfLyhZ0m6qXdvOtYx2bLISjk36xbFJvzg2Wev3tEBI7Lanm1BK1Qs1a9a0BQsWnHGbt+reJZdckuBzvPzyy67iqWDBgjZp0iRr1izmH88AACBj0mp4ntw5Yv76kvv0/8geSmSVzPJdu6zuG2/4vh7UpInd6dfmnz1bNnurXTtrNn68TVi2zJ1E/38wvGlT65vASICsJi2Pi7+lO3a4Qejyv0svTda2AwCAwEs3oZR069bNhg4dap988olrvxMlhC+88IKrZPKui8vkyZNt0KBBVqRIEfv111+tbt26abjlAAAgkLMnk0IDtD0KmhZv/3979wEdVb0tfnxDgARCQgnSpCOgSAdpCuFSvYqi+OdSpFnogor4Hurlga7/w4IK3mu/LMSCCFwQQf9PFKUIJKDSLiUUL52AVAOEEsL8194vZ5yEmQSQnJlkvp+1zsrMnHNmfsOPyczs7L1/ybL+0CHLuFGa2dNi6lQ5nGWBFR3B/G3bpG+DBgEbd4cbN+fF15SMP1iWi46WHnXrXrcx5Ee/nTsnf/3+e2vgfzQ1VWqUKiVDmjaVx1u0sL/CZ0dXopycmCgfbtggvxw/bish3l2rlvx3+/ZSwafMVW07elRufuutgPflGR+8rGoAQOgKqaDUE088IZ988okMHDhQ1q5dK7Vr15ZZs2bJd999J5MmTZLyGR9KNm7caFuDBg1s08DVqFGj7IPRPffcY6vu6ZaVlvLl9OYLIH/q37+//a7IyymwQDgrXuT3FtfnM0q7HE6pV4zPMdlpVrGibBk+XJJPn5bBCxdaxk389OmSNGKEfdH+7x9+8Aak3vzzn2VAo0aSdPSoPDB7tgVJun32mWwcNsxK1MKdm/Pi+zhzt2yxy/fdfLNltsG/9EuXpPMnn3hXR1T6f/nJRYsk+dQpeblTp2zP7z13rnd1RKXlmB+sX29zs37IEAtSOTYePpxLzwIAkJ+FVFCqaNGisnTpUsuW+uijj6xbe506dexyv379vMfNmzfPVssbP368BaW2bdsmO3bssH3Tp0+3zZ9evXpZY2YA4adMmTLBHgKAP0C//MZGRloPo2Nnz3pvv+TxeHsaVStZ8oruq3zx4rbdcsMNlvHRa+5cu9/5SUky7LbbJCGjv2W9smVlRPPm3oCJZpY89c03svnIEdly5IjtD3duzovjh717vSWBGpRCYJrh5ASkxsfHW+8tDUgt+uUXeS0hQQY3bRow608byTsBqT7161tvtTmbN8uEZcssq+2FZcvktS5dLgtKaW+xdUOGuPL8AAB5X8hFaHTFu3/84x+2BTJhwgTbHNqL6nqmjwMAgNDTuHx5WbZnj/UT0gwQzZDRPkQXMxY70MBRIK8nJMjKffvs88K8nj29t+tKb1kzewplZN6cuXDBjneyrPV61mPh3rw4Vmas9qdaVqp0nZ9N/vLZpk32s2x0tPxXfLxl973UsaMFpdI9HpmzZYuMveMOv+eu3r/fVlDUc7THmmbF6X1o6eTJc+dk6Z49mY7fmLFsuZYHVsrjq1cDANxDvjMAAMgTnN5BR1JTLUtDy5Ce+/57uy2iQIHf9585I/tTUmxz6Kpu87Zulc+TkmxFPT130c6dMm7JEu/5d9WqZZe71KxpP3edPCmjM45dsG2bvJHRx6hUVJTUJ0vK9Xlx/Msn+FHyCpebDlc/HTxoP/X/q1Nu2qBcOQs2+e735+nbb5dTzzxjWU9OmWbapUveIKFzH1kzpXReNCCp860ZcwAA5KlMKQDIDf/6178kLS1NChcuzEIIQB41qGlTK0f68eBBeWH5ctt8V2pzypB6zJljmTu+zZWfbdNGFm7fbkGP1xMTbfP1SqdOUiejzFczR77cvt3K9DQrxGmqrfRr/ZQ777xspblw5ta8ZG2IrsEPBKalj04JpZbUOTQ4VapoUesP5dtc3h/9f67llE4Acdz333uDUgMbNcr0WLpAgFP2F/vii3L24kUL4A5r1kwmtGsnhTNWYwQAwBefqACEhW+//VZSUlIkNjaWoBSQRxWJiJDF/fvLhKVLZdbmzbaSmPYrGtq0qTzRsmW252pGzZpHH5UpiYkye8sW+feJE5bpoeVfY1q3lo41amQ6dvWjj1pmlJY//XLihJX0Nb/xRvnP22/PdCzcmxffAIgqXbRorj2n/OC0T7lp1iBqZEaAyOnNlRMtx6z/zjuZgo1DmzXzXv/X4cO2OqU6eOqU93YNik1csUK2HTsm//zLX675uQAA8i+CUgAAIM/Qptqvd+liWyBLBw70e3tMZKSMi4+3LSfRRYpYFo9uCJ15UdtHjrzmcYaT69lv1TejSrMF1yYn20qUjTJWxj6fnm6BRF3R75k77pAet95qmVgPzptnx2rD9MX//jcB3Sx+O3dO/vr99zIvKcmCuZr9N6RpU1tUIacVw9PS02VyYqJlKf5y/LgtOnB3rVq2SIDvapVOj7Z//PyzrZy48/hxK6/UuRvdqpV0v+WWXH6WAJA9glIAAABAPuP0gVLnszSLd0rwYnyOyY42q98yfLgknz4tgxculCW7d0v89OmSNGKEBUDaV68uCY88kukczWR7qUMH6fzJJ3Zde4URlPqdLgqg/zbO6ohKy1h1dUQN7r3cqVO25/eeO9e7OqLSIKAGnXRu1g8ZYkEq7+N8/LG3dNahCwzoNqlTJ8tKBIBgodE5AAAAkM9oUEIz2NSxs2e9t2vzcafXlJZZXonyxYtbbykNPmkmjlNGOT8pKdvzfFfh0x5T+J1mODkBqfHx8Rb0cxZZeC0hwbKfAtG+XU5Aqk/9+rJ5+HCZkJFpqFltuuCAY+amTd6AlC46sGnYMPmmb1+5MSObShclOHz6dC4+UwDIHplSAAAAQD7UuHx5C0hsOHTIMmYiCha0/lBavuVkQAXyekKCZdJoGeC8nj0zlYJlzbh6ZeVKW0FRs3XWDh7szdLRxQIcdeLicuU55lXar06VjY6W/4qPtwb0L3XsKIt++UXSPR6Zs2WLLbrgz+r9+633mp7zXteulhWn96GLMpw8d06W+mRFfbVjh/fyu127WgbbrWXLWongfyxebPOp80wZX3BKK9VHGzbY600z5TSQ/OdatSzL0N+xQH5EUAoAAADIhzQzRoNSR1JTLXumd/36lhmjIgoUsP3qyJkz1hfKN7tJV9ubl5GN89SiRbbKoq6wN27JEu/5d9Wq5V3dL3H/frvcZ948mdi+vT3mf3z7rd2mX7T1sfG7nw4etJ/1y5a14JJqUK6cBZvSLl3y7vfn6dtvl1EtWtjCAE6Zpp7jBAn1Phwvduggg5o0sYCh7+IAmjHn7zLcK61Uf1+9WkZ9/bX3ur5uNEi1cu9eWT90aKYyXPwvAob5D+V7AAAAQD6kgaTbMrKhXli+XG556y35cvt27wp6NUuXtss95syRypMn2+bQJv83lyljl19PTLRz75wxQ/b89pvd9kqnTlInY/+Ahg3lzptussv/b8cOafTee9Lp449l18mT1hj9zT//2QJXEG/po1NC6fvvosGpUhmBI9/m8v7oiopaUukEEEd89ZU3KDWwUSPvcVqiqWWXverVy9RjbNr69d7rTStUuG7PLT9wq7RS/x88mxEk1tVdNw4daq8Vpau+vrZqVS4+y7wdMHzzxx9tpU/N9HMChmMXL87xfA0Y/ufixbLlyBELxDsBw9bTplmwy5cGDAfMny8bDh+2Y52AYZsPPsi0uin+OIJSAAAAQD5UJCJCFvfvL0+2bCkVY2Lseu24OHm9c2crFctOyagoWfPoo/JCu3ZSr2xZKVa4sJSIjLQv59/262crtzm0LHB+z54yuUsXW9VNszv02M41a8qSAQOkX8OGLjzbvMP3C60Gl3xFRkTYz1NX+KVXyzErvPaaTF23zhtsHNqsWcDjtXRTs9m2Hztm1zXbrXqpUtf0PMKltFKDf87rxSmtDMQprdR51NLKujfcYPehryflW1qpAWLn/8J/tW0r9cuVkxHNm0vDcuW8/cCQGQHD/InyPQAAACCf0pKT17t0sS2QpQMH+r09JjJSxsXH25YTDa480bKlbcie9um6XnwzqjQrbW1ysqw/dMiCg1lpVkmfuXO9ZZkaOHzrrruu21jyC7dKK33vp6HPfGmvN83O2XbsmJw6f95eh3C3F5u/gKFu/1i71uZGA4bj27Vz6Vnnf2RKAQgLxYsXl9jYWPsJAAAQLL59grSUzpcTvIi5wl5CGsDQbJHv+ve33jratyh++nTrfeRLH6f7rFneTJGihQrJ5z17XvEKjOHCzdJK3/vxfaw4n95fTrkscg4Y+u4PFDA89cwzsm7IkD8UMFROwBDXB5lSAMLC4MGDgz0EAAAAa66sGWwaADl29mymhuNOQORKg0Xlixe3TYMg2qy519y5dr/zk5Jk2G23eUv2/s+cOd6V+KILF5YFvXvLn6pXz5Xnl5dd79LK+u+8472etbQy02Nl3HfWxyXwceUBQ+0PdbUBw3Hff/+HAoZa2ow/jqAUAAAAALiocfnytjLihkOHrHmz9uXSIIYGkHwzMvzR1cBW7ttnZYDzevbMVJ7ncL5oq8f/53+8De41A+vrvn2ldeXKufTM8jY3SytZ8/DqEDDMvyjfAwAAAAAX9ahb137qil7aYFlXEHsuo7FyRIECv+8/c0b2p6TY5tAMD+0L9XlSkjy1aJGdu2jnThm3ZIn3fG1grpbt3i1v//ST99zXOneWKiVKeO9Tt9S0NFefeyhzs7Qy02MFCCjST8rdgKH3sa7bI+FKkCkFAAAAAC4a1LSprST248GD8sLy5bb5Zm3ULF3aLveYM8cyqpRn/Hj7+WybNrJw+3YLRr2emGibr1c6dZI6ZcrY5VcTEjLtG/zll5eN5YNu3TKVLoUzN0srq8TGeo89lpoqN2ZcP5qa6r29aokS1/HZ5W25ETBMPn1aBi9c6A0YJo0YIRViYi4LGEZlZEgRMMwdZEoBCAsLFy6U2bNn208AAIBgKhIRIYv795cnW7aUijExdr12XJy83rmzrSaWnZJRUbLm0UflhXbtrKdNscKFbSW9LjVryrf9+snoVq3sOC0LXLJrl0vPKH+VViqntFJdTWnlA7NnW1N5X/5KKxtXqOC97efkZO9lJ2OnTlwcgQ8/AUN1PQKGGixsX726BQyVEzBUWQOGDgKGuYNMKQBhYceOHZKSkmIr8AEAAASbfsF+vUsX2wJZOnCg39s1WDEuPt62QLRP1elnn70uYw0nWjqp2WlOaWXv+vUDllY6ZXeVMj5fOqWVSksrNSNuz8mTfksru9aubasgnr14Uf7v8uVW4rdq3z5ZlxGU6l2vXhCefWhzqxdb1oChk8VGwDB3EJQCAAAAAMDF0kpd1W1ihw7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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CUB: 17 and 18 are interesting\n",
    "idx = 18\n",
    "\n",
    "dists = model.explanation(X_test[idx], real_labels[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d85426",
   "metadata": {},
   "source": [
    "# Global Explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "600a3bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "prototypes = model.get_binary_prototypes().cpu().detach().numpy()\n",
    "predictions = predictions.cpu().detach().numpy()\n",
    "uncertainty_matrix = C_hat_test - prototypes[predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "882eeb1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5794, 112)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncertainty_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7a8538a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "present_concept_mask = prototypes[predictions]==1\n",
    "\n",
    "present_uncertainties = uncertainty_matrix * present_concept_mask\n",
    "absent_uncertainties = uncertainty_matrix * ~present_concept_mask\n",
    "\n",
    "concept_presence_counts = np.sum((present_uncertainties) != 0, axis=0)\n",
    "concept_absence_counts = np.sum((absent_uncertainties) != 0, axis=0)\n",
    "\n",
    "avg_present_uncertainty = (np.sum(present_uncertainties, axis=0)) / concept_presence_counts\n",
    "avg_absent_uncertainty = (np.sum(absent_uncertainties, axis=0)) / concept_absence_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c524cc86",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_present_indices = np.argsort(avg_present_uncertainty)\n",
    "sorted_absent_indices = np.argsort(avg_absent_uncertainty)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcc813c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11588, 112)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absent_values = avg_absent_uncertainty[sorted_absent_indices][:5]\n",
    "# present_values = avg_present_uncertainty[sorted_present_indices][:5][::-1]\n",
    "# most_certain_indices = sorted_indices[:5][::-1]\n",
    "# most_uncertain_indices = sorted_indices[::-1][:5]\n",
    "\n",
    "# # # Combine for plotting: most uncertain on the left, most certain on the right\n",
    "# combined_indices = np.concatenate([absent_values, present_values])\n",
    "# combined_values = np.concatenate([absent_uncertainties, present_uncertainties])\n",
    "\n",
    "# combined_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8a278872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most uncertain concepts: [0.05406116 0.05259081 0.05087159 0.04887461 0.04639197]\n",
      "Most uncertain concepts: [ 76  83 100  22  78]\n",
      "\n",
      "Most certain concepts: [0.003 0.003 0.003 0.003 0.004]\n",
      "Most certain concepts: [ 53 102 109  81 105]\n"
     ]
    }
   ],
   "source": [
    "print(\"Most uncertain concepts:\", np.sort(np.mean(np.abs(uncertainty_matrix), axis=0))[::-1][:5])\n",
    "print(\"Most uncertain concepts:\", np.argsort(np.mean(np.abs(uncertainty_matrix), axis=0))[::-1][:5])\n",
    "print()\n",
    "print(\"Most certain concepts:\", np.round(np.sort(np.mean(np.abs(uncertainty_matrix), axis=0))[:5], 3))\n",
    "print(\"Most certain concepts:\", np.argsort(np.mean(np.abs(uncertainty_matrix), axis=0))[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "eb46a7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute mean absolute uncertainty per concept\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "\n",
    "mean_uncertainty = np.mean(np.abs(uncertainty_matrix), axis=0)\n",
    "\n",
    "# Get top 5 most uncertain and top 5 most certain indices and values\n",
    "# sorted_indices = np.argsort(mean_uncertainty)\n",
    "absent_values = avg_absent_uncertainty[sorted_absent_indices][:10]\n",
    "present_values = avg_present_uncertainty[sorted_present_indices][:10]\n",
    "# most_certain_indices = sorted_indices[:5][::-1]\n",
    "# most_uncertain_indices = sorted_indices[::-1][:5]\n",
    "\n",
    "# # Combine for plotting: most uncertain on the left, most certain on the right\n",
    "# combined_indices = np.concatenate([sorted_absent_indices[:5], sorted_present_indices[:5]])\n",
    "# combined_values = np.concatenate([absent_values, np.abs(present_values)])\n",
    "\n",
    "# Create labels\n",
    "labels = [fr'$c_{{{i+1}}}$' for i in sorted_absent_indices[:10]]\n",
    "colors = ['#FF7F7F'] * 10\n",
    "# colors = ['#7FBF7F'] * 10\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12,6))\n",
    "x = np.arange(10)\n",
    "plt.bar(x, absent_values, color=colors, edgecolor='black', linewidth=1, width=0.8)\n",
    "\n",
    "# Annotate values\n",
    "for i, val in enumerate(absent_values):\n",
    "    plt.text(i, val + 0.0001, f'{val:.3f}', ha='center', va='bottom', fontweight='bold', fontsize=14)\n",
    "\n",
    "# Formatting\n",
    "plt.xticks(x, labels, fontsize=14)\n",
    "plt.xlabel('Concept', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.grid(axis='y', linestyle='-', alpha=0.3)\n",
    "limits = plt.ylim()\n",
    "plt.ylim(limits[0],limits[1]+0.03)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel(\"Mean |Uncertainty|\", fontsize=14, fontweight='bold')\n",
    "\n",
    "# plt.axvline(4.5, color='black', linestyle='--', linewidth=2, alpha=0.5)\n",
    "\n",
    "plt.title(f\"Top 10 Most Uncertain Absent Concepts; Average Uncertainty = {np.mean(avg_absent_uncertainty):.2f}\", fontsize=18)\n",
    "legend_elements = [\n",
    "    # Patch(facecolor='white', edgecolor='white', label=f'Average Uncertainty = {np.sum(mean_uncertainty):.2f}'),\n",
    "    # Patch(facecolor='#FF7F7F', edgecolor='black', label='Absent Concept'),\n",
    "    # Patch(facecolor='#7FBF7F', edgecolor='black', label='Present Concept')\n",
    "]\n",
    "\n",
    "# plt.legend(handles=legend_elements, loc='center right', prop={'family': 'sans-serif', 'weight': 'bold', 'size': 14})\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3c317565",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute mean absolute uncertainty per concept\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "mean_uncertainty = np.mean(np.abs(uncertainty_matrix), axis=0)\n",
    "\n",
    "present_values = np.abs(avg_present_uncertainty[sorted_present_indices][:10])\n",
    "\n",
    "# Create labels\n",
    "labels = [fr'$c_{{{i+1}}}$' for i in sorted_present_indices[:10]]\n",
    "# colors = ['#FF7F7F'] * 10\n",
    "colors = ['#7FBF7F'] * 10\n",
    "#  + ['#7FBF7F'] * 5\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12,6))\n",
    "x = np.arange(10)\n",
    "plt.bar(x, present_values, color=colors, edgecolor='black', linewidth=1, width=0.8)\n",
    "\n",
    "# Annotate values\n",
    "for i, val in enumerate(present_values):\n",
    "    plt.text(i, val + 0.0001, f'{val:.3f}', ha='center', va='bottom', fontweight='bold', fontsize=14)\n",
    "\n",
    "# Formatting\n",
    "plt.xticks(x, labels, fontsize=14)\n",
    "plt.xlabel('Concept', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.grid(axis='y', linestyle='-', alpha=0.3)\n",
    "limits = plt.ylim()\n",
    "plt.ylim(limits[0],limits[1]+0.03)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel(\"Mean |Uncertainty|\", fontsize=14, fontweight='bold')\n",
    "\n",
    "\n",
    "plt.title(f\"Top 10 Most Uncertain Present Concepts; Average Uncertainty = {np.mean(np.abs(avg_present_uncertainty)):.2f}\", fontsize=18)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9f69c6",
   "metadata": {},
   "source": [
    "# Alternative Explanation Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "f8bbb123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# if use_dataset != 'CUB':\n",
    "sns.heatmap(prototypes, cmap='Greys', cbar=False, linewidths=0.5, linecolor='lightgray')\n",
    "plt.xlabel(\"Concept Index\")\n",
    "plt.ylabel(\"Prototype Index\")\n",
    "plt.title(\"Binary Prototype Activation Map\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e362b522",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(os.path.join(PROJECT_ROOT, \"output\", use_dataset, \"learned_prototypes.npy\"), prototypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74552f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "from scipy.spatial.distance import pdist\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate Jaccard distance between all pairs\n",
    "dists = pdist(prototypes, metric='jaccard') # pairwise distances\n",
    "Z = linkage(dists, method='average')\n",
    "\n",
    "# Plot only the top N merges (e.g., 30)\n",
    "plt.figure(figsize=(12, 6))\n",
    "dendrogram(Z, truncate_mode='lastp', p=4, show_leaf_counts=True)\n",
    "plt.title(\"Truncated Hierarchical Clustering (Jaccard Distance)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0b9e234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Prototypes in each Cluster ---\n",
      "Cluster 1 (size: 194): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]\n",
      "Cluster 2 (size: 4): [66, 67, 68, 69]\n",
      "Cluster 3 (size: 1): [99]\n",
      "Cluster 4 (size: 1): [50]\n"
     ]
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "NUM_CLUSTERS = 4\n",
    "cluster_labels = fcluster(Z, t=NUM_CLUSTERS, criterion='maxclust')\n",
    "\n",
    "clusters = {}\n",
    "for i, cluster_id in enumerate(cluster_labels):\n",
    "    if cluster_id not in clusters:\n",
    "        clusters[cluster_id] = []\n",
    "    clusters[cluster_id].append(i)\n",
    "\n",
    "print(\"--- Prototypes in each Cluster ---\")\n",
    "for cluster_id, members in sorted(clusters.items()):\n",
    "    print(f\"Cluster {cluster_id} (size: {len(members)}): {members}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2f8cee",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef4c2a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "silhouette_scores = []\n",
    "K_range = range(2, min(21, len(prototypes)))\n",
    "\n",
    "for k in K_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(prototypes)\n",
    "    score = silhouette_score(prototypes, cluster_labels)\n",
    "    silhouette_scores.append(score)\n",
    "\n",
    "# Plot silhouette scores\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(K_range, silhouette_scores, 'bo-')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Optimal Number of Clusters')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d385236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of clusters: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "\n",
    "optimal_k = K_range[np.argmax(silhouette_scores)]\n",
    "print(f\"Optimal number of clusters: {optimal_k}\")\n",
    "\n",
    "# Apply clustering and visualize\n",
    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(prototypes)\n",
    "\n",
    "# PCA + Clustering\n",
    "pca = PCA(n_components=2)\n",
    "pca_result = pca.fit_transform(prototypes)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# t-SNE plot\n",
    "tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(prototypes)-1))\n",
    "tsne_result = tsne.fit_transform(prototypes)\n",
    "\n",
    "scatter = plt.scatter(tsne_result[:, 0], tsne_result[:, 1], c=cluster_labels, cmap='tab10', alpha=0.7)\n",
    "plt.colorbar(scatter)\n",
    "plt.title(f't-SNE with {optimal_k} clusters')\n",
    "plt.xlabel('t-SNE 1')\n",
    "plt.ylabel('t-SNE 2')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ac6ecd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prototypes in cluster 0 (size 27): [19, 46, 54, 74, 76, 87, 95, 97, 155, 156, 160, 161, 162, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 179, 180, 181, 199]\n",
      "Prototypes in cluster 1 (size 33): [1, 40, 44, 52, 58, 59, 61, 62, 63, 64, 65, 70, 71, 78, 79, 80, 82, 83, 85, 93, 100, 140, 142, 143, 144, 145, 146, 158, 186, 188, 189, 190, 191]\n",
      "Prototypes in cluster 2 (size 42): [21, 27, 30, 32, 33, 45, 51, 84, 91, 103, 112, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 129, 130, 132, 134, 148, 149, 153, 177, 182, 183, 192, 193, 194, 195, 196, 197, 198]\n",
      "Prototypes in cluster 3 (size 61): [0, 13, 14, 15, 16, 17, 18, 31, 34, 36, 37, 38, 39, 41, 42, 47, 50, 53, 55, 60, 66, 67, 68, 69, 72, 73, 75, 77, 81, 86, 90, 92, 98, 99, 101, 102, 104, 110, 111, 113, 127, 131, 136, 137, 138, 139, 141, 147, 150, 151, 152, 154, 157, 159, 163, 164, 165, 170, 178, 184, 185]\n",
      "Prototypes in cluster 4 (size 37): [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 20, 22, 23, 24, 25, 26, 28, 29, 35, 43, 48, 49, 56, 57, 88, 89, 94, 96, 105, 106, 107, 108, 109, 133, 135, 187]\n"
     ]
    }
   ],
   "source": [
    "clusters = []\n",
    "for i in range(optimal_k):\n",
    "    clusters.append(np.where(cluster_labels == i)[0].tolist())\n",
    "\n",
    "for i, cluster in enumerate(clusters):\n",
    "    print(f\"Prototypes in cluster {i} (size {len(cluster)}):\", cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "560e7407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prototypes0 = prototypes[clusters[0]]\n",
    "# for concept_idx in range(prototypes0.shape[1]):\n",
    "#     if np.sum(prototypes0[:,concept_idx])==prototypes0.shape[0]:\n",
    "#         print(concept_idx,np.sum(prototypes0[:,concept_idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "940a4428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 50 27.0\n",
      "0 72 27.0\n",
      "1 20 33.0\n",
      "2 50 42.0\n"
     ]
    }
   ],
   "source": [
    "for i, cluster in enumerate(clusters):\n",
    "    protos = prototypes[cluster]\n",
    "\n",
    "    for concept_idx in range(protos.shape[1]):\n",
    "        if np.sum(protos[:,concept_idx])==protos.shape[0]:\n",
    "            print(i, concept_idx,np.sum(protos[:,concept_idx]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
